arXiv:2408.17329v2 Announce Type: replace-cross 
Abstract: Ensuring timely and accurate diagnosis of medical conditions is paramount for effective patient care. Electrocardiogram (ECG) signals are fundamental for evaluating a patient's cardiac health and are readily available. Despite this, little attention has been given to the remarkable potential of ECG data in detecting non-cardiac conditions. In our study, we used publicly available datasets (MIMIC-IV-ECG-ICD and ECG-VIEW II) to investigate the feasibility of inferring general diagnostic conditions from ECG features. To this end, we trained a tree-based model (XGBoost) based on ECG features and basic demographic features to estimate a wide range of diagnoses, encompassing both cardiac and non-cardiac conditions. Our results demonstrate the reliability of estimating 23 cardiac as well as 21 non-cardiac conditions above 0.7 AUROC in a statistically significant manner across a wide range of physiological categories. Our findings underscore the predictive potential of ECG data in identifying well-known cardiac conditions. However, even more striking, this research represents a pioneering effort in systematically expanding the scope of ECG-based diagnosis to conditions not traditionally associated with the cardiac system.

تستكشف الدراسة المنشورة على arXiv إمكانية استخدام إشارات تخطيط القلب (ECG) في تشخيص الحالات القلبية وغير القلبية. باستخدام مجموعات بيانات مثل MIMIC-IV-ECG-ICD و ECG-VIEW II، قام الباحثون بتدريب نموذج قائم على الأشجار (XGBoost) لتقدير مجموعة متنوعة من التشخيصات. تشير النتائج إلى موثوقية تقدير 23 حالة قلبية و21 حالة غير قلبية بمعدل AUROC يتجاوز 0.7، مما يبرز الدور المهم لبيانات ECG في التشخيصات الطبية الأوسع.

El estudio publicado en arXiv investiga el potencial de las señales de electrocardiograma (ECG) para diagnosticar condiciones cardíacas y no cardíacas. Utilizando conjuntos de datos como MIMIC-IV-ECG-ICD y ECG-VIEW II, los investigadores entrenaron un modelo basado en árboles (XGBoost) para estimar una variedad de diagnósticos. Los resultados muestran una estimación confiable de 23 condiciones cardíacas y 21 no cardíacas con un AUROC superior a 0.7, destacando el papel significativo de los datos de ECG en diagnósticos médicos más amplios.

L'étude publiée sur arXiv examine le potentiel des signaux d'électrocardiogramme (ECG) dans le diagnostic des conditions cardiaques et non cardiaques. En utilisant des ensembles de données tels que MIMIC-IV-ECG-ICD et ECG-VIEW II, les chercheurs ont formé un modèle basé sur des arbres (XGBoost) pour estimer une variété de diagnostics. Les résultats indiquent une estimation fiable de 23 conditions cardiaques et 21 conditions non cardiaques avec un AUROC supérieur à 0,7, soulignant le rôle significatif des données ECG dans les diagnostics médicaux plus larges.

The study published on arXiv explores the potential of electrocardiogram (ECG) signals in diagnosing both cardiac and non-cardiac conditions. Utilizing datasets like MIMIC-IV-ECG-ICD and ECG-VIEW II, researchers trained a tree-based model (XGBoost) to estimate a variety of diagnoses. Results indicate a reliable estimation of 23 cardiac and 21 non-cardiac conditions with an AUROC above 0.7, highlighting the significant role of ECG data in broader medical diagnostics.

Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features

arXiv:2506.08274v5 Announce Type: replace-cross 
Abstract: This research addresses the critical lack of comprehensive studies on feature scaling by systematically evaluating 12 scaling techniques - including several less common transformations - across 14 different Machine Learning algorithms and 16 datasets for classification and regression tasks. We meticulously analyzed impacts on predictive performance (using metrics such as accuracy, MAE, MSE, and $R^2$) and computational costs (training time, inference time, and memory usage). Key findings reveal that while ensemble methods (such as Random Forest and gradient boosting models like XGBoost, CatBoost and LightGBM) demonstrate robust performance largely independent of scaling, other widely used models such as Logistic Regression, SVMs, TabNet, and MLPs show significant performance variations highly dependent on the chosen scaler. This extensive empirical analysis, with all source code, experimental results, and model parameters made publicly available to ensure complete transparency and reproducibility, offers model-specific crucial guidance to practitioners on the need for an optimal selection of feature scaling techniques.

دراسة حديثة نُشرت على arXiv قامت بتقييم منهجي لـ 12 تقنية لتقييس الميزات عبر 14 خوارزمية تعلم آلي و16 مجموعة بيانات، كاشفة عن تباينات كبيرة في الأداء في نماذج مثل الانحدار اللوجستي وSVM، بينما أظهرت طرق التجميع قوة مستقلة عن التقييس.

Un estudio reciente publicado en arXiv evaluó sistemáticamente 12 técnicas de escalado de características a través de 14 algoritmos de aprendizaje automático y 16 conjuntos de datos, revelando variaciones significativas en el rendimiento de modelos como la regresión logística y los SVM, mientras que los métodos de ensamblaje mostraron robustez independientemente del escalado.

Une étude récente publiée sur arXiv a évalué systématiquement 12 techniques de mise à l'échelle des caractéristiques à travers 14 algorithmes d'apprentissage automatique et 16 ensembles de données, révélant des variations de performance significatives dans des modèles tels que la régression logistique et les SVM, tandis que les méthodes d'ensemble ont montré une robustesse indépendamment de la mise à l'échelle.

A recent study published on arXiv systematically evaluated 12 feature scaling techniques across 14 machine learning algorithms and 16 datasets, revealing significant performance variations in models like Logistic Regression and SVMs, while ensemble methods showed robustness regardless of scaling.

Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features

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